Surface Defect incorporated Diamond Machining of Silicon

This paper reports the performance enhancement benefits in diamond turning of the silicon wafer by incorporation of the Surface Defect Machining (SDM) method. The hybrid micromachining methods usually require additional hardware to leverage the added advantage of hybrid technologies such as laser heating, cryogenic cooling, electric pulse or ultrasonic elliptical vibration. The SDM method tested in this paper does not require any such additional baggage and is easy to implement in a sequential micro-machining mode. This paper made use of Raman spectroscopy data, average surface roughness data and imaging data of the cutting chips of silicon for drawing a comparison between conventional Single Point Diamond Turning (SPDT) and SDM while incorporating surface defects in the (i) circumferential and (ii) radial directions. Complimentary 3D Finite Element Analysis (FEA) was performed to analyse the cutting forces and the evolution of residual stress on the machined wafer. It was found that the surface defects generated in the circumferential direction with an interspacing of 1 mm revealed the lowest average surface roughness (Ra) of 3.2 nm as opposed to 8 nm Ra obtained through conventional SPDT using the same cutting parameters. The observation of the Raman spectroscopy performed on the cutting chips showed remnants of phase transformation during the micromachining process in all cases. FEA was used to extract quantifiable information about the residual stress as well as the sub-surface integrity and it was discovered that the grooves made in the circumferential direction gave the best machining performance. The information being reported here is expected to provide an avalanche of opportunities in the SPDT area for low-cost machining solution for a range of other nominal hard, brittle materials such as SiC, ZnSe and GaAs as well as hard steels

Surface defects incorporated diamond machining of silicon

Abstract This paper reports the performance enhancement benefits in diamond turning of the silicon wafer by incorporation of the surface defect machining (SDM) method. The hybrid micromachining methods usually require additional hardware to leverage the added advantage of hybrid technologies such as laser heating, cryogenic cooling, electric pulse or ultrasonic elliptical vibration. The SDM method tested in this paper does not require any such additional baggage and is easy to implement in a sequential micro-machining mode. This paper made use of Raman spectroscopy data, average surface roughness data and imaging data of the cutting chips of silicon for drawing a comparison between conventional single-point diamond turning (SPDT) and SDM while incorporating surface defects in the (i) circumferential and (ii) radial directions. Complementary 3D finite element analysis (FEA) was performed to analyse the cutting forces and the evolution of residual stress on the machined wafer. It was found that the surface defects generated in the circumferential direction with an interspacing of 1 mm revealed the lowest average surface roughness (Ra) of 3.2 nm as opposed to 8 nm Ra obtained through conventional SPDT using the same cutting parameters. The observation of the Raman spectroscopy performed on the cutting chips showed remnants of phase transformation during the micromachining process in all cases. FEA was used to extract quantifiable information about the residual stress as well as the sub-surface integrity and it was discovered that the grooves made in the circumferential direction gave the best machining performance. The information being reported here is expected to provide an avalanche of opportunities in the SPDT area for low-cost machining solution for a range of other nominal hard, brittle materials such as SiC, ZnSe and GaAs as well as hard steels.</jats:p

Abstract Texture image segmentation using combined features from spatial and spectral distribution

Communicated by T.K. Ho Texture discrimination is playing a vital role in a real world image classification and object identification in a content based image retrieval (CBIR) system. For discriminating the textures, exact features have to be extracted. Although there are many techniques available they are not capable of classifying the universal textures because of their inherent limitations. In this paper, a novel method is introduced to extract the features by combining the texture discriminating features of spatial and spectral distribution of image attributes, and a comparison is made with the popular Gaussian and Gabor wavelets based methods for segmenting the image. The segmented outputs and the classification efficiency of the proposed method are found to be better and the time taken is reasonable. Ó 2005 Elsevier B.V. All rights reserved